Learning a CNN with Class Specific Backpropagation Computer
Learning a CNN with Class. Specific Backpropagation Computer Vision Lab. Hyeonseob Nam
Motivation • Learning an object detector is often disturbed by noisy regions in the training data.
Goal • Build a robust object detector by learning a CNN with class-specific regions.
Related Works Maxime Oquab et al. , Weakly supervised object recognition with convolutional neural networks, NIPS 2014
Related Works Deepak Pathak et al. , Fully Convolutional Multi-Class Multiple Instance Learning, ar. Xiv 2015
Related Works Karen Simonya et al. , Deep inside convolutional networks: Visualising image classification models and saliency maps, ar. Xiv 2014
Forward-propagation in a CNN
Back-propagation in a CNN
Back-propagation in a CNN
Back-propagation in a CNN
Class-specific Back-propagation in a CNN
Class-specific Back-propagation in a CNN
Class-specific Back-propagation in a CNN
Class-specific Back-propagation in a CNN
Class-specific Back-propagation in a CNN input image pool 3 pool 4 pool 5 score
Outline Pretraining in Imagenet Finetuning with class-specific backpropagation rcnn-based detection
Thank you
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